VR teaching method and VR teaching system

Abstract

The invention provides a VR teaching method and a VR teaching system. The VR teaching method comprises the following steps: acquiring request information; sending image assistance information to each data provider; each data provider provides an image group of the data provider to the data center according to the image assistance information; the data center splices the obtained image groups of all the data providers, so that all complete image information is obtained; the data center renders each piece of obtained complete image information so as to obtain a rendered image; and the data center synthesizes the rendered image and the audio data into audio and video data and sends the audio and video data to a data provider requesting the data. According to the VR teaching method, the video information (formed by combining the images) required by teaching is distributed in a plurality of data providers, so that each data provider cannot acquire the complete video information, and leakage of the video information is prevented.

Description

Technical Field

[0001] This invention belongs to the field of VR teaching technology, specifically a VR teaching method and a VR teaching system. Background Technology

[0002] VR (Virtual Reality) technology encompasses computer science, electronic information, and simulation technology. Its basic implementation involves using computers to simulate virtual environments, thereby creating a sense of immersion. With the continuous development of social productivity and science and technology, the demand for VR technology is increasingly strong across all industries. VR technology has also made significant progress and is gradually becoming a new field of scientific and technological research.

[0003] With the continuous development and advancement of VR technology, various VR applications and devices have emerged in the market. Among them, VR teaching management systems that combine VR technology with teaching are one of the most commonly used applications. Currently, the common practice of VR teaching management systems is to distribute VR courseware to students' devices and then render and display it on those devices. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a VR teaching method to solve the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a VR teaching method, the VR teaching method comprising:

[0006] The data center obtains request information from any data provider requesting VR courseware;

[0007] The data center sends image assistance information to various data providers based on the request information;

[0008] Each data provider provides its image set to the data center according to the image assistance information, wherein each image set includes multiple image information containing secret shares and each image information identification code, and each image information containing secret shares is incomplete image information;

[0009] The data center stitches together the image information containing secret shares from the image groups obtained from various data providers to obtain complete image information.

[0010] The data center renders each complete image piece of information it acquires to obtain a rendered image;

[0011] The data center combines rendered images and audio data into audio-visual data and sends it to the data provider that requested the data.

[0012] Optionally, the VR teaching method further includes:

[0013] The data center sends audio assistance information to various data providers based on the request information;

[0014] Each data provider provides its audio sets to the data center based on the audio assistance information, wherein each audio set includes multiple audio messages containing secret shares and each audio message identification code, and each audio message containing secret shares is an incomplete audio message;

[0015] The data center splices together the audio information containing secret shares from the audio groups obtained from various data providers to obtain the complete audio data.

[0016] Optionally, before the data center obtains request information from any data provider requesting VR courseware, the VR teaching method further includes:

[0017] The data center acquires an original image set, which includes multiple original images;

[0018] The data center acquires the number of data providers it can cooperate with;

[0019] The data center randomly segments each original image based on the number of data providers that can cooperate, thereby creating fewer incomplete image information from each original image than from the data providers. Each incomplete image information has an image information identification code.

[0020] The data center randomly distributes each incomplete image information to a data provider and stores a tuple consisting of the image information identification code of each incomplete image information and the identification code of the data provider to which the incomplete image information was sent.

[0021] Optionally, before the data center obtains request information from any data provider requesting VR courseware, the VR teaching method further includes:

[0022] The data center acquires a raw audio set, which includes multiple raw audio segments.

[0023] The data center acquires the number of data providers it can cooperate with;

[0024] Based on the number of data providers that can cooperate, the data center randomly divides each original audio segment into segments, thereby creating incomplete audio information with fewer segments than the data providers. Each incomplete audio segment has an audio information identification code.

[0025] The data center randomly distributes each incomplete audio message to a data provider and stores a tuple consisting of the audio message identification code of each incomplete audio message and the identification code of the data provider to which the incomplete audio message was sent.

[0026] Optionally, after the data center synthesizes the rendered images and audio data into audio-visual data and sends it to the data provider requesting the data, the VR teaching method further includes:

[0027] The data center resends each incomplete image information to its respective data provider, ensuring that each data provider does not retrieve previously acquired incomplete image information and reassembles the audio information identification code of each incomplete image information with the identification code of the data provider to which the incomplete image information was sent, forming a tuple for storage.

[0028] Optionally, after the data center synthesizes the rendered images and audio data into audio-visual data and sends it to the data provider requesting the data, the VR teaching method further includes:

[0029] The data center resends each incomplete audio message to its respective data provider, ensuring that each data provider does not retrieve previously acquired incomplete audio messages and reassembles the audio message identification code of each incomplete audio message with the identification code of the data provider to which the incomplete audio message was sent, forming a tuple for storage.

[0030] Optionally, rendering each of the acquired complete images to obtain a rendered image includes:

[0031] Edge detection algorithms are used to extract edge information from the acquired images;

[0032] Edge information is identified to obtain the proportion of edge pixels and the degree of edge curvature.

[0033] Texture analysis algorithms are used to extract texture features from the acquired images, and texture complexity is obtained by calculating the entropy, contrast, and correlation of the texture features.

[0034] The acquired image is analyzed to determine the shape of objects within the image, thereby obtaining the complexity of the geometric shape;

[0035] The scale level is obtained based on the obtained edge pixel ratio information, edge curvature information, texture complexity, and geometric complexity.

[0036] Rendering is performed using multi-scale rendering techniques based on the obtained scale level.

[0037] Optionally, the step of extracting texture features from the acquired image using a texture analysis algorithm, and obtaining texture complexity by calculating the entropy, contrast, and correlation of the texture features, includes:

[0038] Obtain the gray-level co-occurrence matrix of the image;

[0039] Calculate the entropy, contrast, and correlation based on the obtained gray-level co-occurrence matrix;

[0040] The texture complexity is determined based on the obtained entropy value, contrast, and correlation.

[0041] Optionally, the entropy value is calculated using the following formula:

[0042]

[0043] in,

[0044] E″ is the entropy value, L is the number of gray levels, i.e., the number of possible gray values ​​in the image, P(i,j) is the probability of a pixel pair with gray values ​​i and j appearing in the gray-level co-occurrence matrix, and ∈ is a very small positive number used to smooth the probability values. The sum of the probabilities of all pixel pairs plus the smoothing factor ∈ is represented by λ, which is an adjustment parameter used to control the influence of the gray-level difference weight. |ij| represents the gray-level difference, that is, the absolute difference between the two gray values ​​in a pixel pair, used to measure the gray-level difference between pixel pairs. This represents the square root of the weighted sum of the squares of the grayscale differences, used as a normalization factor to ensure that the weights of the grayscale differences are within a reasonable range.

[0045] This application also provides a VR teaching system, which includes a data center and multiple data providers; wherein the data center and the various data providers cooperate to implement the VR teaching method described above.

[0046] The VR teaching method of this application distributes the video information (composed of images) required for teaching across multiple data providers, thereby preventing any data provider from obtaining complete video information and preventing the leakage of video information. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating a VR teaching method according to an embodiment of this application. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] like Figure 1 The VR teaching methods shown include:

[0050] Step 1: The data center obtains request information from any data provider requesting VR courseware;

[0051] Step 2: The data center sends image assistance information to each data provider based on the request information;

[0052] Step 3: Each data provider provides its image set to the data center according to the image assistance information, wherein each image set includes multiple images containing secret shares and each image information identification code, and each image information containing secret shares is incomplete image information;

[0053] Step 4: The data center stitches together the image information containing secret shares from the image groups obtained from various data providers to obtain complete image information.

[0054] Step 5: The data center renders the acquired complete image information to obtain the rendered image;

[0055] Step 6: The data center combines the rendered images and audio data into audio and video data and sends it to the data provider that requested the data.

[0056] The VR teaching method of this application distributes the video information (composed of images) required for teaching across multiple data providers, thereby preventing any data provider from obtaining complete video information and preventing the leakage of video information.

[0057] In this embodiment, the VR teaching method further includes:

[0058] The data center sends audio assistance information to various data providers based on the request information;

[0059] Each data provider provides its audio sets to the data center based on the audio assistance information, wherein each audio set includes multiple audio messages containing secret shares and each audio message identification code, and each audio message containing secret shares is an incomplete audio message;

[0060] The data center splices together the audio information containing secret shares from the audio groups obtained from various data providers to obtain the complete audio data.

[0061] In this embodiment, before the data center obtains request information from any data provider requesting VR courseware, the VR teaching method further includes:

[0062] The data center acquires an original image set, which includes multiple original images;

[0063] The data center acquires the number of data providers it can cooperate with;

[0064] The data center randomly segments each original image based on the number of data providers that can cooperate, thereby creating fewer incomplete image information from each original image than from the data providers. Each incomplete image information has an image information identification code.

[0065] The data center randomly distributes each incomplete image information to a data provider and stores a tuple consisting of the image information identification code of each incomplete image information and the identification code of the data provider to which the incomplete image information was sent.

[0066] In this embodiment, before the data center obtains request information from any data provider requesting VR courseware, the VR teaching method further includes:

[0067] The data center acquires a raw audio set, which includes multiple raw audio segments.

[0068] The data center acquires the number of data providers it can cooperate with;

[0069] Based on the number of data providers that can cooperate, the data center randomly divides each original audio segment into segments, thereby creating incomplete audio information with fewer segments than the data providers. Each incomplete audio segment has an audio information identification code.

[0070] The data center randomly distributes each incomplete audio message to a data provider and stores a tuple consisting of the audio message identification code of each incomplete audio message and the identification code of the data provider to which the incomplete audio message was sent.

[0071] In this embodiment, after the data center synthesizes the rendered image and audio data into audio-visual data and sends it to the data provider requesting the data, the VR teaching method further includes:

[0072] The data center resends each incomplete image information to its respective data provider, ensuring that each data provider does not retrieve previously acquired incomplete image information and reassembles the audio information identification code of each incomplete image information with the identification code of the data provider to which the incomplete image information was sent, forming a tuple for storage.

[0073] In this embodiment, after the data center synthesizes the rendered image and audio data into audio-visual data and sends it to the data provider requesting the data, the VR teaching method further includes:

[0074] The data center resends each incomplete audio message to its respective data provider, ensuring that each data provider does not retrieve previously acquired incomplete audio messages and reassembles the audio message identification code of each incomplete audio message with the identification code of the data provider to which the incomplete audio message was sent, forming a tuple for storage.

[0075] In this embodiment, rendering each of the acquired complete images to obtain a rendered image includes:

[0076] Edge detection algorithms are used to extract edge information from the acquired images;

[0077] Edge information is identified to obtain the proportion of edge pixels and the degree of edge curvature.

[0078] Texture analysis algorithms are used to extract texture features from the acquired images, and texture complexity is obtained by calculating the entropy, contrast, and correlation of the texture features.

[0079] Perform illumination analysis on the acquired image to obtain the illumination model used in the image;

[0080] The acquired image is analyzed to determine the shape of objects within the image, thereby obtaining the complexity of the geometric shape;

[0081] The overall rendering complexity of the image is determined based on the obtained edge pixel ratio information, edge curvature information, texture complexity, lighting model, and complexity.

[0082] The scale level is determined based on the overall rendering complexity.

[0083] Rendering is performed using multi-scale rendering techniques based on the obtained scale level.

[0084] In this embodiment, edge detection algorithms can use methods such as the Sobel operator and Canny edge detection.

[0085] In this embodiment, the information on the degree of curvature of the edge can be obtained using the following method:

[0086] The degree of tortuosity of an edge can be quantified by calculating the frequency or magnitude of directional changes in the chain code. For example, the total number of adjacent directional changes in the chain code can be calculated, or the entropy value of the chain code sequence can be calculated.

[0087] In this embodiment, the ratio information of edge pixels can be obtained in the following way:

[0088] Count the number of edge pixels and calculate their ratio to the total number of pixels in the image.

[0089] In this embodiment, a quantitative index can be generated for the proportional information of edge pixels and the curvature information of the edge, for example, using the following formula:

[0090] Q = a × edge pixel ratio + b × edge curvature, where Q is the final quantification index, and a and b are weighting coefficients that can be set according to your own needs.

[0091] In this embodiment, the step of extracting texture features from the acquired image using a texture analysis algorithm, and obtaining texture complexity by calculating the entropy, contrast, and correlation of the texture features, includes:

[0092] Obtain the gray-level co-occurrence matrix of the image;

[0093] Calculate the entropy, contrast, and correlation based on the obtained gray-level co-occurrence matrix;

[0094] The texture complexity is determined based on the obtained entropy value, contrast, and correlation.

[0095] In this embodiment, the entropy value is calculated using the following formula:

[0096]

[0097] in,

[0098] E″ is the entropy value, L is the number of gray levels, i.e., the number of possible gray values ​​in the image, P(i,j) is the probability of a pixel pair with gray values ​​i and j appearing in the gray-level co-occurrence matrix, and ∈ is a very small positive number used to smooth the probability values. The sum of the probabilities of all pixel pairs plus the smoothing factor ∈ is represented by λ, which is an adjustment parameter used to control the influence of the gray-level difference weight. |ij| represents the gray-level difference, that is, the absolute difference between the two gray values ​​in a pixel pair, used to measure the gray-level difference between pixel pairs. This represents the square root of the weighted sum of the squares of the grayscale differences, used as a normalization factor to ensure that the weights of the grayscale differences are within a reasonable range.

[0099] The improved formula for calculating entropy in this application has the following advantages:

[0100] Improve the quantization accuracy of image details:

[0101] By combining the concepts of gray-level difference and entropy, the weighted normalized gray-level difference entropy technique can more accurately quantify the levels of detail in an image. Gray-level difference reflects the variation in gray-level values ​​between adjacent pixels in an image, while entropy measures the complexity of image information. This combination allows the technique to capture more comprehensive detail information in an image.

[0102] Enhance image adaptability:

[0103] This formula can automatically adjust weights and parameters according to different image scenes, thereby improving the algorithm's adaptability. This allows the technology to be more widely applied to various image processing and analysis tasks, such as image enhancement, image segmentation, and image retrieval.

[0104] Improve image exposure:

[0105] This formula, by calculating the weighted normalized gray-level entropy, can accurately find the optimal exposure time corresponding to scene features. This helps improve the exposure effect of the image, enabling the image to achieve its best state in both overall scene and local detail. For example, in complex scenes, this technique can ensure that the image does not lose important information and enhances both the overall scene and local details, thereby improving image quality.

[0106] Improve the robustness and reliability of the algorithm:

[0107] The weighted normalized gray-level difference entropy technique reduces the impact of outliers on the algorithm results by weighting and normalizing the gray-level difference and entropy. This makes the technique more robust and reliable in practical applications, enabling it to handle various complex image scenes and noise interference.

[0108] Optimize computational efficiency and resource utilization:

[0109] Although the weighted normalized grayscale difference entropy technique involves a relatively complex calculation process, with proper algorithm design and optimization, computational efficiency can be improved while maintaining computational accuracy. Furthermore, this technique can be combined with other image processing algorithms to jointly optimize resource utilization and computational performance.

[0110] In this embodiment, the texture complexity metric can be obtained using the following formula:

[0111] To comprehensively quantify the texture complexity of an image, the three indicators mentioned above can be weighted and summed to obtain a texture complexity index. Assuming the weights are w1, w2, and w3 (and w1 + w2 + w3 = 1), the texture complexity index T can be expressed as:

[0112] T = w1·H(X) + w2·I(d, θ) + w3·Correlation; By adjusting the weights, different texture features can be emphasized according to actual needs. For example, if more attention is paid to the complexity of the texture, the weight of the entropy value can be increased appropriately; if more attention is paid to the clarity of the texture and the depth of the grooves, the weight of the contrast can be increased appropriately. I(d, θ) represents contrast, and Correlation represents correlation.

[0113] In this embodiment, the complexity of the shape of the set can be obtained by the box dimension calculation method, wherein the larger the value of the fractal dimension D, the higher the complexity.

[0114] The process of obtaining the scale level based on the acquired edge pixel ratio information, edge curvature information, texture complexity, and geometric complexity includes:

[0115] The weighted values ​​are calculated by weighting the edge pixel ratio, edge curvature, texture complexity, and geometric complexity.

[0116] Set multiple threshold ranges, for example, set three threshold ranges, namely the threshold range corresponding to the first scale, the threshold range corresponding to the second scale, and the threshold range corresponding to the third scale. When the weighted value falls into a certain threshold range, it corresponds to that scale.

[0117] The rendering method described in this application can significantly improve rendering efficiency, enhance image quality, and expand the application scenarios of rendering technology.

[0118] This application also provides a VR teaching system, which includes a data center and multiple data providers; wherein the data center and the various data providers cooperate to implement the VR teaching method described above.

[0119] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0120] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A VR teaching method, characterized in that, The VR teaching method includes: The data center obtains request information from any data provider requesting VR courseware; The data center sends image assistance information to various data providers based on the request information; Each data provider provides its image set to the data center according to the image assistance information, wherein each image set includes multiple image information containing secret shares and each image information identification code, and each image information containing secret shares is incomplete image information; The data center stitches together the image information containing secret shares from the image groups obtained from various data providers to obtain complete image information. The data center renders each complete image piece of information it acquires to obtain a rendered image; The data center combines rendered images and audio data into audio-visual data and sends it to the data provider that requested the data.

2. The VR teaching method as described in claim 1, characterized in that, The VR teaching method further includes: The data center sends audio assistance information to various data providers based on the request information; Each data provider provides its audio sets to the data center based on the audio assistance information, wherein each audio set includes multiple audio messages containing secret shares and each audio message identification code, and each audio message containing secret shares is an incomplete audio message; The data center splices together the audio information containing secret shares from the audio groups obtained from various data providers to obtain the complete audio data.

3. The VR teaching method as described in claim 2, characterized in that, Before obtaining request information from any data provider requesting VR courseware from the data center, the VR teaching method further includes: The data center acquires an original image set, which includes multiple original images; The data center acquires the number of data providers it can cooperate with; The data center randomly segments each original image based on the number of data providers that can cooperate, thereby creating fewer incomplete image information from each original image than from the data providers. Each incomplete image information has an image information identification code. The data center randomly distributes each incomplete image information to a data provider and stores a tuple consisting of the image information identification code of each incomplete image information and the identification code of the data provider to which the incomplete image information was sent.

4. The VR teaching method as described in claim 3, characterized in that, Before obtaining request information from any data provider requesting VR courseware from the data center, the VR teaching method further includes: The data center acquires a raw audio set, which includes multiple raw audio segments. The data center acquires the number of data providers it can cooperate with; Based on the number of data providers that can cooperate, the data center randomly divides each original audio segment into segments, thereby creating incomplete audio information with fewer segments than the data providers. Each incomplete audio segment has an audio information identification code. The data center randomly distributes each incomplete audio message to a data provider and stores a tuple consisting of the audio message identification code of each incomplete audio message and the identification code of the data provider to which the incomplete audio message was sent.

5. The VR teaching method as described in claim 4, characterized in that, After the rendered images and audio data are combined into audio-visual data in the data center and sent to the data provider requesting the data, the VR teaching method further includes: The data center resends each incomplete image information to its respective data provider, ensuring that each data provider does not retrieve previously acquired incomplete image information and reassembles the audio information identification code of each incomplete image information with the identification code of the data provider to which the incomplete image information was sent, forming a tuple for storage.

6. The VR teaching method as described in claim 5, characterized in that, After the rendered images and audio data are combined into audio-visual data in the data center and sent to the data provider requesting the data, the VR teaching method further includes: The data center resends each incomplete audio message to its respective data provider, ensuring that each data provider does not retrieve previously acquired incomplete audio messages and reassembles the audio message identification code of each incomplete audio message with the identification code of the data provider to which the incomplete audio message was sent, forming a tuple for storage.

7. The VR teaching method as described in claim 6, characterized in that, The process of rendering each acquired complete image to obtain a rendered image includes: Edge detection algorithms are used to extract edge information from the acquired images; Edge information is identified to obtain the proportion of edge pixels and the degree of edge curvature. Texture analysis algorithms are used to extract texture features from the acquired images, and texture complexity is obtained by calculating the entropy, contrast, and correlation of the texture features. The acquired image is analyzed to determine the shape of objects within the image, thereby obtaining the complexity of the geometric shape; The scale level is obtained based on the obtained edge pixel ratio information, edge curvature information, texture complexity, and geometric complexity. Rendering is performed using multi-scale rendering techniques based on the obtained scale level.

8. The VR teaching method as described in claim 7, characterized in that, The process of extracting texture features from the acquired image using a texture analysis algorithm, and obtaining texture complexity by calculating the entropy, contrast, and correlation of the texture features, includes: Obtain the gray-level co-occurrence matrix of the image; Calculate the entropy, contrast, and correlation based on the obtained gray-level co-occurrence matrix; The texture complexity is determined based on the obtained entropy, contrast, and correlation values.

9. The VR teaching method as described in claim 8, characterized in that, The entropy value is calculated using the following formula: ; in, E″ is the entropy value, L is the number of gray levels, i.e., the number of possible gray values ​​in the image, P(i,j) is the probability of a pixel pair with gray values ​​i and j appearing in the gray-level co-occurrence matrix, and ∈ is a very small positive number used to smooth the probability values. The sum of the probabilities of all pixel pairs plus the smoothing factor ∈ is represented by λ, which is an adjustment parameter used to control the influence of the gray-level difference weight. |ij| represents the gray-level difference, that is, the absolute difference between the two gray values ​​in a pixel pair, used to measure the gray-level difference between pixel pairs. This represents the square root of the weighted sum of the squares of the grayscale differences, used as a normalization factor to ensure that the weights of the grayscale differences are within a reasonable range.

10. A VR teaching system, characterized in that, The VR teaching system includes a data center and multiple data providers; wherein the data center and the various data providers cooperate to implement the VR teaching method as described in any one of claims 1 to 9.

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